Minimization Algorithm

The
Minimization algorithm
selects the next treatment arm for a randomized
participant by looking at the existing distributions of pre-existing
randomizations. The algorithm attempts to minimize the difference between
these distributions by assigning the participant to the treatment arm with
the lowest number of participants who fit the stratification factors. In
this way, the algorithm can ensure an even distribution of participants by
stratification factor to all treatment arms.

The
Minimization algorithm
also introduces a chance for a random treatment arm selection which is set
to 30% by default.

The
Chance of Random Treatment Arm Selection
can be altered when setting up a new randomization scheme, and changed to
any integer between 0 (Never Random) and 100 (Always Random).

Generating Lists

Under the
Minimization algorithm,
only one list of randomization treatment arm assignments exists, unless
site is used as a stratification factor. This is to ensure that within a
site, the distribution of participants across treatment arms is balanced.

When a subject is randomized, the next treatment assignment is dynamically
generated based on the distribution of stratification factors of previous
participants on the list. Since it is not possible to know the distributions
in advance, it is not possible to generate a pre-determined list of
treatment assignments at the start of a study when using the
Minimization algorithm.